Many communication devices utilize a channel estimate to accurately detect data within a signal received from a communication channel. In one approach, the channel estimate is used to determine a set of channel taps to be used by an equalizer to remove certain channel effects from the received signal. In systems where the channel characteristics do not change significantly over time (i.e., time invariant channels), a single set of channel taps may be determined at the start of transmission (or before the start of transmission) and used for the duration of the subsequent communication. Such a technique is referred to as preset equalization. In systems where channel characteristics are expected to change with time (e.g., in mobile communication systems), channel tap adjustments are typically made periodically or continuously during the communication in a process known as adaptive equalization. One form of adaptive equalization that is commonly implemented in communication systems uses the well known least-mean-square (LMS) algorithm to adaptively modify the channel taps. The LMS algorithm is an iterative technique that utilizes a noisy estimate of an error gradient during each iteration to adjust the estimated channel taps in a manner that reduces average mean-square error (MSE). As can be appreciated, techniques and structures for enhancing the effectiveness and/or accuracy of LMS-based adaptive equalization are generally desired.